This maximum amount of offset days that will be assigned to an item’s safety stock calculation.

A common misconception is that one could limit the amount of safety stock being calculated by merely setting limits for the offset days. However, the risk percentage also plays a huge role in calculating the safety stock days. These values are not dependent on each other.

When using these parameters, it is important to determine whether the business has a bias towards over or under forecasting and whether accumulating excess stock or stocking out will be more detrimental.

Take note that changing these global settings may mask granular risk calculations for certain items where it would have been beneficial to explore and correct the cause of the high risk percentage.

Refer to this article for a detailed explanation of Supply Risk and Offset and this article for a detailed explanation of how the Recommended order quantity is calculated.

Let’s look at Maximum (%), Minimum (%), Maximum offset, Minimum offset, Lead time cut-off (deviation) and Lead time cut-off (%) collectively.

Risk offset indicates the additional days that need to be added to or subtracted from the calculated safety stock days.

To determine how risky a supplier is considered to be, we need to look at the performance of the supplier. Do they deliver on time and in full? Or do they frequently deliver late and in short supply?

Let’s suppose we have an item with a planning lead time of 14 days. It means an order for this item from this supplier will be recommended (and hopefully actioned) 14 days before the item is due to be sold. If, in reality, this item from this supplier has had an AVERAGE delivery duration of 50 days, then it means the item will arrive 36 days late when placed 14 days in advance. A risk offset value of 36 days will therefore be calculated. This means that an additional 36 days worth of safety stock will be added to the already calculated safety stock value in order to cover the risk associated with this inaccurate planning lead time. This is the simple explanation - there are many nuances we won’t be covering in this article.

What exactly is the risk percentage? This indicated the degree of variability in the data. Simply put, how “all-over-the-place” are the data points the app has to work with?

In the example above, we calculated that historically, the delivery duration (lead time) has been 50 days. This could mean that out of the 4 deliveries this supplier made historically, one arrived in 51 days, another in 48 days, another in 49 days and the last one in 52 days. That will result in an average of 50 days.

However, it could also mean that out of the 4 deliveries this supplier made historically, one arrived in 16 days, another in 17 days, another in 90 days and the last one in 77 days. That will also result in an average of 50 days, but those data points are all over the place, making it hard for the app to know whether the order will be severely late or somewhat late and plan accordingly.

A common misconception is that a large risk percentage results in a large risk offset. This is not necessarily the case as these two values are calculated (mostly) independently.

And how does all this affect the calculation of safety stock?

We know that Safety stock is calculated using the following 5 factor:

These factors mostly impact each other in order to calculate the required safety stock, except for risk offset days, which gets dealt with after.

It’s therefore possible to have a large risk percentage (data points are all over the place), but a low risk offset (the average of the historical lead time matches the planning lead time or the supplier over supplies).

It’s also possible to have a low risk percentage (all the data points indicate a historical lead time of 50 days) and a large risk offset (the planning lead time is only 14 days. 36 days need to be added to the safety stock).

Limiting the Minimum Risk % and Maximum Risk % as well as the Minimum Risk Offset and Maximum Risk Offset is therefore crucial when the goal is to avoid having safety stock calculated to be 300 days (and thus keep 300 days worth of capital tied up in stock). However, if not stocking out is the goal, then it may be preferred to not have cut-offs in place for these parameters.

Remember: You can set cut-offs for safety stock in the Configuration settings too.

What about Lead time cut-off? Lead time cut-off aims to exclude data which may skew our calculation of the average lead time. Risk percentage calculates the degree of variability. Now imagine having a historic lead time data point so large that it ends up increasing your risk percentage and thus your safety stock. For this reason it could be useful setting the cut-off threshold in terms of a percentage or standard deviation.

Suppose our average historic supplier lead time has been calculated to be 50 days. Setting a lead time cut-off percentage of 60% means that any data points where the lead time was more than 80 days or less than 20 days will be ignored. Why those values?

Cut-off percentage * average lead time days = cut-off days

60% of an average of 50 days = 30 days.

Average lead time days + cut-off days = upper data limit

50 + 30 = 80 days.

Average lead time days - cut-off days = lower data limit

50 - 30 = 20 days

Suppose our average historic supplier lead time has been calculated to be 50 days. Setting a lead time cut-off standard deviation of 3 means that any data points where the lead time fell outside of 3 standard deviations of the mean will be ignored.

NOTE: The app uses the more generous of the two cut-offs when excluding outliers. This means the measured lead time of an order must lie outside the threshold of both cut-offs. This can make it challenging to exclude extreme readings when there are very few readings, due to very high standard deviations.

NOTE: These cut-off settings only apply to the supply risk calculation. The lead time measurement calculation uses an internal fixed cut-off of 3 standard deviations.

Question: How does one calculate standard deviation?

Answer: Standard deviation is a complex, yet common mathematical calculation that can be investigated in more detail on any mathematical website or in any mathematical textbook.

Question: Why would I use lead time cut-off percentage instead of lead time cut-off deviation?

Answer: Why would you drive a Ferrari instead of a Lamborghini? It makes no difference to me, but a car enthusiast may provide you with a longer, more detailed answer. Similarly, mathematicians will argue the benefits of using standard deviation cut-off over percentage cut-off, but in the greater scheme, it doesn't make that much of a difference. Set both to be safe to account for fringe data points.